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Review 2: "Understanding the Key Determinants of an HPV Therapeutic Vaccine: A Modeling Analysis"

The reviewers found this study makes a good case in favour of such potential interventions to prevent cervical cancer. However, they also expressed concerns regarding some assumptions made by the model and how these might impact the results.

Published onMar 04, 2024
Review 2: "Understanding the Key Determinants of an HPV Therapeutic Vaccine: A Modeling Analysis"
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Understanding the key determinants of an HPV therapeutic vaccine: a modeling analysis
Understanding the key determinants of an HPV therapeutic vaccine: a modeling analysis

Abstract Despite incredibly effective tools to prevent HPV infection and treat precancerous lesions, the scale-up of existing interventions in most low and middle-income countries has been slow, leaving a residual burden of invasive cervical cancer that will persist for decades. An HPV therapeutic vaccine may overcome some of the scalability and infrastructure challenges of traditional screening and treatment programs, though its potential public health value depends upon its characteristics, delivery strategy, and the underlying immunity of the population on which it would act. This analysis uses HPVsim, an open-access agent-based simulation framework, to evaluate the impact of a range of potential HPV therapeutic vaccines with varying scale-up of existing preventive interventions in nine high-burden low- and middle-income countries (LMICs). For each setting, the model is populated with context-specific demographic and behavioral data, and calibrated to fit estimates of HPV and cervical disease by age. We find that an HPV therapeutic vaccine that clears 90% of virus and regresses 50% of high-grade lesions, reaching 70 percent of 35-45 year old women starting in 2030, could avert 1.2-2.2 million incident cases of cervical cancer, 500,000-1.2 million cervical cancer deaths and 20-40 million disability adjusted life years (DALYs) in the modeled high-burden LMICs over 30 years. The size of the impact is sensitive to rates of background intervention scale-up and the characteristics of the vaccine, including ability to establish long-lasting immune memory.

RR:C19 Evidence Scale rating by reviewer:

  • Reliable. The main study claims are generally justified by its methods and data. The results and conclusions are likely to be similar to the hypothetical ideal study. There are some minor caveats or limitations, but they would/do not change the major claims of the study. The study provides sufficient strength of evidence on its own that its main claims should be considered actionable, with some room for future revision.


Review: In this study, the authors use HPV sim, an open-access agent-based simulation framework, to investigate the impact of a potential HPV therapeutic vaccine in India, Indonesia, Bangladesh, Myanmar, Ethiopia, Nigeria, the Democratic Republic of Congo, Uganda, and Tanzania. The model is calibrated with the demographic features for each country based on population estimates, birth and death rates, age, and sex from the UN's World Population Projections. Furthermore, the authors used HPVsim's built-in sexual network algorithm to roughly fit sexual behavior data and then calibrated key components of the HPV natural history to fit cervical cancer cases by age and HPV genotype distribution in pre-cancer and invasive cervical cancer. The parameter estimation is done using Optuna, a Bayesian hyperparameter optimization algorithm, and the goodness-of-fit is computed as the sum of normalized absolute differences between the model's outputs and the data. Although the authors calibrated the model with care, it is important to remark that there is no quality data for population-representative sexual behavior in the countries of interest, and the sensitivity of the results depending on this data is not explored in enough detail. 

The majority of assumptions related to prophylactic vaccination, screening & treatment are realistic. However, there is one assumption that might be strong and with the potential to affect the model outcomes: it is assumed for all scenarios that there is a 90% coverage of PxV, but it is also assumed that routine screening and treatment are delayed or absent. In particular, for scenario 1, it is assumed that the screen and treatment coverage is 0%. How much are the results (e.g. DALYs averted) if the authors omit Scenario 1? In general, even if the quantitative results might vary depending on some assumptions, to my eye, this study has successfully shown that an HPV therapeutic vaccine might bring benefits to LMICs.

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